Sari Kusuma Dewi, Yuni Sri Rahayu, Putut Rakhmad Purnama, Yuliani, Mahanani Tri Asri, Ali Imron
The identification of plant-based biostimulants requires integrative approaches that link community-derived knowledge with experimental validation. This study integrates social media knowledge mining, high-performance liquid chromatography (HPLC) phytohormone profiling, and germination bioassays to identify plant-derived biostimulants. Indonesian local plants were screened from TikTok, Instagram, and YouTube based on growth-related claims, then processed into aqueous filtrates. Endogenous indole-3-acetic acid (IAA), gibberellic acid (GA3), and trans-zeatin were quantified using HPLC, and biological efficacy was evaluated through mung bean (Vigna radiata) germination assays at different filtrate concentrations. The results revealed pronounced interspecific variation in phytohormone profiles and significant improvements in germination percentage, uniformity, seedling growth rate, and vigor index for several filtrates, particularly at 50-75% concentrations. Multivariate analyses demonstrated clear associations between hormonal composition and germination performance. This integrated framework provides a robust and scalable approach for identifying plant-based biostimulants by bridging digital knowledge mining with analytical and biological validation. © School of Engineering, Taylor’s University.
Universitas Negeri Surabaya, Surabaya, Indonesia
